What Is Statistical Analysis Software?

Statistical analysis software programs have built-in tools to help you analyze and visualize data to spot trends and patterns. Statistical analysis helps you and any stakeholders make informed decisions using the data collected from a survey or other forms of research. Statistical analysis is a part of data analysis and allows you to collect data through research, use statistical methods to model data and design further studies to collect better or different data. Statistical computing opens up a range of possibilities for statisticians and data scientists to process data and make informed decisions. 

Explore statistical analysis software, the different types available to you, how you can use it, the careers and industries that find it helpful, and how you can start learning about statistical analysis. 

Types of statistical analysis software

Many different types of statistical analysis software exist, from open-source solutions like R to enterprise solutions like IBM SPSS Statistics. Learn more about some different types of statistical analysis software below. 

SPSS

SPSS Statistics is a statistical analysis software developed by IBM. It has a suite of tools aimed at different experience levels. SPSS Statistics includes various features and an intuitive interface so that you can organize large data sets, use statistical techniques, make predictions, and produce research. Other benefits of SPSS include:

• A user interface with drag-and-drop functions that bypass the need for coding

• Tools for data management, cleanup, and manipulation

• Analytics tools to perform both descriptive and predictive modeling

• A suite of data visualization tools

• Open-source integration of packages using R and Python

Cost: Starts at an online subscription of $99 per month with additional add-ons [1]

SAS

SAS/STAT (Statistical Analysis System) is a high-performing enterprise statistical analysis software with constant updates, providing you with the newest models and best methods. Other features of SAS include:

• A detailed library of more than 100 statistical analysis procedures with pre-written code

• A range of statistical methods for specialized needs to enterprise-level analysis

• Scalability and a wide range of support on many major computing systems

• Regular updates with high-performing tools and a system to process user requests

Cost: Request pricing directly from SAS.

R

R is a programming language developed for statistical computing and graphics. It is an open-source alternative to the S programming language. R is not only a programming language but an environment with a fully functional system around the language to support statistical computing. Other features of the R environment include:

• Ways to handle data and the storage of data

• Many kinds of mathematical operators for arrays and matrices

• A fully functional programming language with loops, conditionals, and the ability for users to create their own functions 

• Various ways to display graphics (you may view them within the integrated development environment, or IDE, or print a physical copy)

• Tools available to perform data analysis

You can use R with RStudio, an IDE that helps you write the R code. 

Cost: Both R programming language and RStudio are free software.

Minitab

Minitab Statistical Software allows you to find patterns in data, make predictions, and visualize your data to find relationships. Minitab integrates data analytics and visualizations to help you discover, predict, and achieve your statistical goals. Other features of Minitab include:

• A combination of software and cloud resources to power your business

• A series of statistical modeling, correlation, and time series analysis tools

• The ability to integrate Python and R languages for more intensive machine learning algorithms to make predictions

• Real-time integration of graphics that allow you to change your visualization without running your analysis over and over

Cost: Free trial; basic yearly subscriptions start at $1,851 for a single user [2]

Stata

Stata is a fast and accurate integrated software with data manipulation, visualization, and statistical tools that includes automated reporting. Some features of Stata include:

• A large suite of statistical tools and modeling

• Full Python integration that allows you to use any Python package

• Continual updates that provide you with the most recent models and data tools

• A full graphical user interface (GUI) so no coding is necessary to make use of its features

• A suite of resources, including tutorial videos, training, webinars, and blogs

Cost: A business single-user yearly subscription is $925 [3]

What is statistical analysis software used for?

Statistical analysis software can aid you in improving the efficiency of statistical analysis for your business. Although this process could be done without the help of software, using statistical analysis software allows you to enjoy a more streamlined approach to statistical analysis, reducing the risk of error. You may use statistical software for many tasks, such as:

• Analyzing marketing performance to improve strategies and efficiency

• Using data analysis to find opportunities in patterns you could not see otherwise

• Making decisions based on models and forecasting to improve planning

• Optimizing business processes by finding inefficiencies through modeling and prediction

Statistical analysis is a key component that you can use to perform data analysis and make informed decisions. It’s an important part of business intelligence, a field that uses data so that you can learn about your business’s function in the market and gain an advantage over its competitors.

Who uses statistical analysis software?

You can find statistical analysis software in many industries, such as psychology, data science, economics, education, epidemiology, business, marketing, medicine, public health, and politics. Due to the implementation of big data, statistics is an in-demand skill as companies analyze this data. Explore how a few key industries use statistical and data analysis below: 

• Manufacturing: You may utilize statistics to make informed decisions and predictions about market futures using your business's varying sources of data.

• Health care: If you work in the health care or pharmaceutical industries, you can use statistics to improve patient treatments, drug therapies, and efficiency in the overall patient experience.

• Government:You can use statistics in many different fields of government, whether it's to improve services, make economic forecasts, improve environmental protections, or strengthen public health outcomes. 

Careers in statistical analysis

According to the US Bureau of Labor Statistics, the job outlook for mathematicians and statisticians from 2023 to 2033 is 11 percent, and the outlook for data scientists over the same period is 36 percent [4, 5]. Both of these figures are much faster than the average for all jobs in the US. Below is a list of many different kinds of jobs that use statistical analysis and their average annual salary:

*All salary information is the median annual base pay from Glassdoor as of October 2024.

• Data scientist: $115,496

• Market researcher: $60,863

• Statistician: $99,917

• Financial analyst: $79,487

• Actuary: $135,130 

• Biostatistician: $100,651

• Machine learning engineer: $121,221

• Economist: $122,793

Advantages of using statistical analysis software

Statistical analysis software helps you produce more efficient statistical calculationsmore quickly than doing them by hand. It can help provide you and your business with other benefits:

• Creates data visualizations in the software, making them easy to analyze by many stakeholders

• Allows for the processing of much larger, faster datasets

• Leads to informed data-driven decision-making in an organization

Things to consider when choosing statistical analysis software

When choosing a statistical analysis software, you will want to consider various factors, including price, subscription, and coding requirements. Consider the following factors when you compare statistical analysis software:

• Cost: Enterprise software like SAS and Minitab have much higher price points than IBM SPSS and free software like RStudio.

• Coding: Consider whether each software has coding integrations from sources like R, Python, or C++. If you need a fully custom environment, consider RStudio, which has many package integrations.

• Tools: Research the kinds of statistical tools and models each software offers and whether you interact with them through a GUI or through code.

• Subscription: Many paid statistical analysis software have a monthly or yearly subscription for a certain number of seats. Determine your needs and those of your organization as you examine each one. 

How to get started in statistical analysis 

To start using statistical analysis software, many companies provide free trials that you can use to see if a certain software fits your needs. If you’re new to using statistical analysis, you may need to learn some of the basics of statistics before entering into statistical analysis software. Explore some of the educational paths and skills you’ll need to work in statistical analysis. 

Educational paths

To become a mathematician or statistician, you’ll want at least a bachelor’s degree, but most jobs require a master’s degree in either mathematics or statistics. Becoming a data scientist requires a similar level of education, with a bachelor’s degree in math, statistics, or computer science being the most common path. However, many employers in this field prefer candidates with a master’s or doctoral degree. 

Developing skills and expertise

While earning a degree is the formal educational path to learning statistical analysis methods, you can develop skills or hone your expertise through self-study or online courses. To self-study statistics, focus on the essential concepts in statistics such as:

• Descriptive statistics

• Probability distribution

• Hypothesis testing

• Regression analysis

Exploring statistics can help you build upon the statistical analysis skills you use in data science. If you want to build skills in statistics through online learning, consider the Introduction to Statistics course from Stanford on Coursera.

Getting started in statistical analysis software with Coursera

Statistical analysis software is a core component to producing efficient and effective data organization, analysis, and visualization. If you want to gain in-demand skills in statistical analysis and data analytics, try the Google Data Analytics Professional Certificate on Coursera. To gain skills directly in some statistical analysis software, try the Statistics with SAS course directly from SAS or the Data Analysis with R Specialization from Duke University, both on Coursera.